{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy\n",
    "x1=numpy.ones((4,4,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[1. 1. 1. 1.]\n",
      "  [1. 0. 0. 1.]\n",
      "  [1. 0. 0. 1.]\n",
      "  [1. 1. 1. 1.]]\n",
      "\n",
      " [[1. 0. 0. 1.]\n",
      "  [0. 0. 0. 0.]\n",
      "  [0. 0. 0. 0.]\n",
      "  [1. 0. 0. 1.]]\n",
      "\n",
      " [[1. 0. 0. 1.]\n",
      "  [0. 0. 0. 0.]\n",
      "  [0. 0. 0. 0.]\n",
      "  [1. 0. 0. 1.]]\n",
      "\n",
      " [[1. 1. 1. 1.]\n",
      "  [1. 0. 0. 1.]\n",
      "  [1. 0. 0. 1.]\n",
      "  [1. 1. 1. 1.]]]\n"
     ]
    }
   ],
   "source": [
    "x1[3,2,1]=0\n",
    "print(x1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 1. 1. 1. 1. 0. 0. 1.]\n",
      " [1. 0. 0. 1. 1. 1. 1. 1.]\n",
      " [1. 0. 0. 1. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 1. 0. 0. 1.]\n",
      " [1. 0. 0. 1. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 1. 0. 0. 1.]\n",
      " [1. 1. 1. 1. 1. 0. 0. 1.]\n",
      " [1. 0. 0. 1. 1. 1. 1. 1.]]\n"
     ]
    }
   ],
   "source": [
    "x2=x1.reshape(8,8)\n",
    "print(x2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# 绘制热力图\n",
    "im = ax.imshow(x2)\n",
    "\n",
    "# 添加颜色条\n",
    "fig.colorbar(im)\n",
    "\n",
    "# 显示图形\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.3"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
